Systems and methods for parking space selection based on weighted parameter comparison

ABSTRACT

Parking space selection systems and methods are disclosed. One or more computing devices are configured to determine a first listing value based on one or more listing parameters associated with a first listing, which is secured by a first wireless device. A second listing value is determined based on one or more listing parameters associated with a second listing. The first and second listing value are compared and a difference between them is determined. It is determined whether the difference between the first listing value and the second listing value exceeds a predetermined threshold. In response to the determination that the difference exceeds the predetermined threshold, a user-selectable message is provided on the first wireless device. The user-selectable message includes a prompt configured to secure the second listing. In response to the prompt, the second listing is secured for the first wireless device.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. ProvisionalApplication No. 62/611,100 filed on Dec. 28, 2017, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to wireless device navigation,and more specifically, to securing listings with increased listing valuecompared to other listings for user devices.

BACKGROUND

Parking a vehicle in densely populated environments is often afrustrating and time-consuming experience, with few available parkingspaces and major street congestion. This is especially true in populatedurban environments such as the downtowns of large municipalities (e.g.,New York City, San Francisco, etc.). Moreover, parking is often indemand near destinations or event venues such as neighborhoodssurrounding sports stadiums, concert halls, amusement parks, orbeachfronts.

Additionally, the high price of real estate has motivated many propertyowners to seek out non-traditional ways for property owners to monetizetheir real property assets. For example, homeowners can often rent outrooms in their homes to tourists or travelers using an online homerental platform; car drivers can provide taxi services using theirpersonal vehicle to pedestrians using a ride sharing platform.

With traditional booking systems, a user may manually search for betterparking—finding spaces that may be closer and cheaper. With thosesystems, the user will have to cancel their current booking and hopethat a cheaper and closer space might still be available.

SUMMARY

A parking space selection system and methods of operation are disclosed.According to one embodiment, a system includes a non-transitory memoryand one or more processors coupled thereto; the one or more processorsare configured to perform operations comprising: determining a firstlisting value based on one or more listing parameters associated with afirst listing, the first listing being secured by a first wirelessdevice; determining a second listing value based on one or more listingparameters associated with a second listing, comparing the first andsecond listing value to determine a difference between them, determiningwhether the difference between the first listing value and the secondlisting value exceeds a predetermined threshold; in response to thedetermination that the difference exceeds the predetermined threshold,providing a user-selectable message on the first wireless device, theuser-selectable message including a prompt configured to secure thesecond listing; and securing the second listing for the first wirelessdevice. According to another embodiment, securing the second listingfurther comprises receiving a user selection of the second listing viathe prompt and releasing a hold on the first listing such that the firstlisting is no longer secured by the first wireless device. According toyet another embodiment, each of the one or more listing parameters isassociated with a respective weight that is configured to be dynamicallyadjusted using machine learning. According to yet another embodiment,each of the one or more listing parameters is associated with arespective weight that is dynamically adjusted in response to thesecuring of the second listing. According to yet another embodiment, thepredetermined threshold is zero. According to yet another embodiment,the predetermined threshold is time dependent. According to yet anotherembodiment, the user-selectable message includes a deep link. Accordingto yet another embodiment, the first listing value is based on a sum ofone or more listing parameters. According to yet another embodiment, theone or more listing parameters include one or more of a proximity of thefirst wireless device to listings, temporal duration of reservationperiod, price range for listings, or a type of listing.

According to another embodiment, a computer-implemented method forsecuring listings with increased listing value includes, determining afirst listing value based on one or more listing parameters associatedwith a first listing, the first listing being secured by a firstwireless device; determining a second listing value based on one or morelisting parameters associated with a second listing, comparing the firstlisting value with the second listing value to determine a differencebetween the first listing value and the second listing value,determining whether the difference between the first listing value andthe second listing value exceeds a predetermined threshold; in responseto a determination that the difference exceeds the predeterminedthreshold, providing a user-selectable message on the first wirelessdevice, the user-selectable message comprising a prompt configured tosecure the second listing; and securing the second listing for the firstwireless device. According to yet another embodiment, securing thesecond listing further comprises receiving a user selection of thesecond listing via the prompt and releasing a hold on the first listingsuch that the first listing is no longer secured by the first wirelessdevice. According to yet another embodiment, each of the one or morelisting parameters is associated with a respective weight that isconfigured to be dynamically adjusted using machine learning. Accordingto yet another embodiment, each of the one or more listing parameters isassociated with a respective weight that is dynamically adjusted inresponse to the securing of the second listing. According to yet anotherembodiment, each of the one or more listing parameters is associatedwith a respective weight that is dynamically adjusted based on one ormore user configurable settings. According to yet another embodiment,the first listing value is based on a weighted sum of the one or morelisting parameters. According to yet another embodiment, the one or morelistings are filtered based on the one or more listing parameters anddisplayed on a display of the first wireless device.

According to another embodiment, a system includes a non-transitorymemory and one or more processors coupled thereto. The one or moreprocessors are configured to perform operations comprising: determininga first listing value based on a first weighted sum of one or morelisting parameters associated with a first listing, the first listingbeing secured by a first wireless device; determining a second listingvalue based on a second weighted sum of one or more listing parametersassociated with a second listing, comparing the first listing value withthe second listing value to determine a difference between the firstlisting value and the second listing value; in response to adetermination that the first listing value exceeds the second listingvalue, providing a user-selectable message on the first wireless devicethe user-selectable message comprising a prompt configured to secure thesecond listing; and securing the second listing for the first wirelessdevice. According to yet another embodiment, the second listing issecured by a second wireless device, and the operations further compriseproviding a user-selectable message on the second wireless device, theuser-selectable message comprising a prompt configured to secure thefirst listing. According to yet another embodiment, the user-selectablemessage includes an incentive option. According to yet anotherembodiment, the operations further comprise determining that the firstlisting value is in a first range of listing values and the secondlisting value is in a second range of listing values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example diagram of a distributed computing system accordingto some embodiments.

FIG. 2 is an example diagram of a distributed computing networkaccording to some embodiments.

FIG. 3 is an example system with a user interface for presenting one ormore listings on a map displayed on a device.

FIG. 4 is a flowchart illustrating an example method securing a firstlisting for a first user device, determining a second listing has asecond listing value within a range different than the first listing,and securing the second listing for the first user device.

FIG. 5 is a flowchart illustrating an example method for determininglisting values for respective listings and securing listings withincreased listing value.

FIG. 6 is an example system illustrating conceptually how listings maybe ranked for a first user device.

FIG. 7 is a flowchart illustrating an example method for determininglistings with increased listing value and facilitating a mutual exchangeof secured listings.

In the figures, elements having the same designations have the same orsimilar functions.

DETAILED DESCRIPTION

Using shared economy parking applications, smartphone owners can useon-demand parking systems to pay property owners for use of theirparking space(s). However, applications rely on user input to determinearrival and departure of vehicles in parking spaces, or on expensivesensors. Accordingly, it would be desirable to provide improved methodsand systems of providing options to automatically reserve parking spaceswith greater value to those smartphone users. The foregoing problems areaddressed by aspects of the subject technology that provide systems andmethods for identifying better parking options, and for updating aparking reservation based on user configured preferences.

In some aspects, parking space listings (or listings) are compared basedon an overall value computed as a function of one or more respectiveparameters (or listing parameters). A value can refer to a quantifiedcomposite value based on numeric and weighted assignments for differentparameters. A listing parameter (or parameter) can refer to anyquantitative or qualitative characteristic associated with a particularlisting. By way of example, parking parameters may include featuresincluding, but not limited to: parking space cost, parking spacelocation, parking space size, a distance from the user, and/or bookinghistory, etc. As discussed in further detail below, the calculated valuefor a particular listing can be based on a function of weightedparameters. Parameter weights may be chosen based on a variety offactors including user-selected settings and/or user reservation historyinformation. In some aspects, parameter weights may be calibrated usinga machine-learning method.

In practice, suppose that a user reserved a parking space (e.g., a firstlisting reservation). Later, it is determined that a second listing isavailable and that the second listing is potentially more desirable tothe user, e.g., because it is cheaper and/or closer to the user's finaldestination. A software application may be used to filter spaces forinstance, that have already been suggested and rejected, spaces whichhave too low of a user rating, compact spaces, spaces that are too far,and based on other listing parameters. This application may weigh thedifferent listing parameters based on parking spaces the user hasreserved historically or based on indicated user preferences. Thisapplication may filter parking spaces and then present the user with theoption to secure a second, available listing on their smartphone. Theuser may opt for the second listing, and the first listing then becomesavailable for another user to secure. In some scenarios, the filteringand comparing may take place on the application server(s) or thesmartphone; the smartphone may receive the option to secure the secondlisting on the smartphone and the application server(s) may receive aresponse that causes the application server(s) to secure the secondlisting for the smartphone. The application server(s) could also simplyfilter, compare, and update the second listing for the smartphonewithout requiring any input from the smartphone. Once the second listingis secured for the smartphone, the first listing may then be releasedand become available for other users to secure.

Suppose also that a user reserved a first parking space, when a secondparking space with a higher listing value is not available because it isreserved by another, second user. The intelligent parking system maycalculate that the second parking space is better for the first user,and offer an option to the first user to reserve the second listinginstead. At the same time, the intelligent parking system may alsocalculate that the first parking space is better for the second user,and offer the option to reserve the first parking space. If both usersagree to the exchange, the reservations are swapped. In such a way,better parking options may be provided through the intelligent parkingsystem to a greater number of users. In practice, suppose that thesecond user liked his current listing reservation more than the firstlisting option. The intelligent parking system may offer incentives tothe second user to encourage the second user to exchange listingreservations with the first user-incentives such as a coupons orcredits.

Such intelligent parking systems increase efficiency with respect toparking, reduce traffic, reduce accidents, diminish or eliminate theneed for vehicle operator (i.e., user) input, more accurately predictparking needs, and lead to faster processing of parking transactions. Byshortening the time spent looking for parking for drivers, less carswill be on the road looking for parking, significantly decreasingtraffic. Furthermore, intelligent parking systems decrease walkingdistance for users from their parking spot to their destination anddecrease costs associated with parking.

In some embodiments, a listing value refers to a metric for quantifyinga listing's relative value compared with other listings. In someexamples, a listing value may be on a scale of 1-100. A particularlisting value may be calculated based on parameters and weights. In someexamples, the listing value is a weighted sum of the parameters andassociated weights. Each parameter, which is a quantitative orqualitative characteristic, may be related to characteristics such asproximity to the physical location of a second location, such as a userlocation or point of interest; an availability time, proximity ofavailability time to another reference time, a type of listing,dimensions associated with listings, dimensions of vehicles associatedwith respective user devices, information about any hazards, whether ornot the listing has a garage, information about price and time, such asprice per hour, price for various times during a day, price per day,price per week/month/year; relative size, a price range, whether or notthe listing has an over-head cover, whether or not the listing is anelectric vehicle (EV) charging station; past, current, and/or futuredemand for the listing; a user rating, such as 1-5; whether parkingenforcement is available, the responsiveness of parking enforcement,and/or the like and/or a combination thereof. In some examples, aparameter's value may be binary (i.e., 0 or 1) depending on whether aparticular characteristic is associated with a listing or not. In someexamples, a parameter's value may be an integer (e.g., 1, 2, 3, etc.),for example, each type of listing may be assigned an integer: a compactspace may be valued as 1, an SUV-sized space may be valued as a 2, alarge truck space may be valued as a 3, and so on. In some examples, aparameter such as price may have a parameter value in dollars; suchparameter may be weighted by a multiplier that, when applied to theparameter value (i.e., the amount of dollars), it equals some numberwhich, when added with other numbers that are related to otherparameters and weights, equals the listing value. Listing valuecalculations are further described in the discussion of FIG. 5.

In some embodiments, weights that are applied to parameters may bepreconfigured by an administrative system or application server, may beuser selectable, and/or adjusted by machine-learning algorithms usingdata from previously reserved listings. Weight value calculations arefurther described in the discussion of FIG. 5.

FIG. 1 is a simplified diagram of a distributed computing system 100according to some embodiments. As shown in FIG. 1, system 100 includesthree computing devices 110, 140, and 170. One of ordinary skill wouldappreciate that distributed computing system 100 may include any numberof computing devices of various types and/or capabilities. In someembodiments, computing devices 110, 140, and/or 170 may be any type ofcomputing device including personal computers (e.g., laptop, desktop,smartphone, or tablet computers), servers (e.g., web servers, databaseservers), network switching devices (e.g. switches, routers, hubs,bridges, and/or the like), vehicle-based devices (e.g., on-board vehiclecomputers, short-range vehicle communication systems, telematicsdevices), or mobile communication devices (e.g., mobile phones, portablecomputing devices, and/or the like), and/or the like, and may includesome or all of the elements previously mentioned.

In some embodiments, computing device 110 includes a control unit 120coupled to memory 130; computing device 140 includes a control unit 150coupled to memory 160; and computing device 170 includes a control unit180 coupled to memory 190. Each of control units 120, 150, and/or 180may control the operation of its respective computing device 110, 140,and/or 170. In some examples, control units 120, 150, and/or 180 mayeach include one or more processors, central processing units (CPUs),graphical processing units (GPUs), virtual machines, microprocessors,microcontrollers, logic circuits, hardware finite state machines (FSMs),digital signal processors (DSPs) application specific integratedcircuits (ASICs), field programmable gate arrays (FPGAs), and/or thelike and/or combinations thereof. In some examples, memory 130 may beused to store one or more applications and one or more data structures,such as application 132 and data structure 134. In some examples, memory160 may be used to store one or more applications and one or more datastructures, such as application 162 and data structure 164, and memory190 may be used to store one or more applications and one or more datastructures, such as application 192 and data structure 194.

In some embodiments, memories 130, 160, and/or 190 may each include oneor more types of machine-readable media, including volatile andnon-volatile memory. Some common forms of machine-readable media mayinclude floppy disk, flexible disk, hard disk, magnetic tape, any othermagnetic medium, CD-ROM, any other optical medium, punch cards, papertape, any other physical medium with patterns of holes, ROM, PROM,EPROM, FLASH-EPROM, any other memory chip or cartridge, and/or any othermedium from which a processor or computer is adapted to read. Somecommon forms of volatile memory include SRAM, DRAM, IRAM, and/or anyother type of medium which retain data while devices are powered,potentially losing the memory when the devices are not powered.

The data structures 134, 164, and/or 194 may vary in size, usage, and/orcomplexity depending upon the purposes of computing devices 110, 140,and/or 170 and/or applications 132, 162, and/or 192. In someembodiments, when computing devices 110, 140, and/or 170 are networkswitching devices, such as switches, routers, hubs, bridges, and/or thelike, the data structures 134, 164, and/or 194 may include one or moretables with forwarding and/or similar information. In some examples,these tables may include one or more virtual local area network (LAN)tables, link aggregation group (LAG) tables, layer 2 (L2) next hoptables, layer 3 (L3) routing tables, L3 forwarding information bases(FIBs), flow tables, and/or the like. Depending upon the networkingenvironment of system 100 and/or the role of computing devices 110, 140,and/or 170 these tables may include anywhere from a few dozen entries tothousands, or even tens of thousands or more entries. In some examples,data from data structures 134, 164, and/or 194 may be retrieved, stored,or modified by a respective control unit in accordance with instructionswhich may be executed directly, e.g., machine code, or indirectly, e.g.,scripts, by the respective control unit. The systems and methods of thepresent disclosure are not limited to any particular data structure.

In some embodiments, computing devices 110, 140, and 170 may also becoupled together using a network 101. In some embodiments, one or moreof computing devices 110, 140, and 170 may be connected via any type ofwired or wireless connections, such as dedicated short-rangecommunications (DSRC), satellite, radio-frequency identification (RFID),fire wire, network, USB, Wi-Fi, RFID, BLUETOOTH, Near FieldCommunication (NFC), Infrared (e.g., GSM infrared), and/or the likeand/or using any suitable wireless communication standards andprotocols, such as IEEE 802.11 and WiMAX. Network 101, including anyintervening nodes, may be any kind of network including a LAN, such asan Ethernet, a wide area network (WAN) such as an internet, a virtual ornon-virtual private network, and/or the like and/or combinationsthereof.

In some embodiments, network 101 may include any type of computingdevice including personal computers (e.g., laptop, desktop, smartphone,or tablet computers), servers (e.g., web servers, database servers),network switching devices (e.g. switches, routers, hubs, bridges, and/orthe like), vehicle-based devices (e.g., on-board vehicle computers,short-range vehicle communication systems, telematics devices), ormobile communication devices (e.g., mobile phones, portable computingdevices, and/or the like), and/or the like, and may include some or allof the elements previously mentioned. Computing devices 110, 140, and170 through their applications, such as applications 132, 162, and/or192, may use network 101 to exchange information and/or to provideservices for each other. In some examples, computing device 140 may beused to provide backup and/or fail over services for computing device110. In some examples, computing device 140 may be maintaining datastructure 164 as a synchronized copy of data structure 134. In someexamples, one or more of components of computing devices 110, 140, and170, such as a control unit, may be located remotely.

In some embodiments, computing devices 110, 140, and/or 170 may includean electronic display, the display may be an active matrix emittingdiode (AMOLED), light-emitting diode (LED), organic LED (OLED),electrophoretic, liquid crystal, e-paper, and/or the like and/orcombinations thereof.

In some embodiments, computing devices 110, 140, and/or 170 may includevarious input and output (I/O) devices, such as a keyboard, a mouse,touchscreen, button inputs, microphone, motion sensor, eye sensor, videodisplay, and/or the like.

FIG. 2 is a simplified diagram of a distributed computing system 200. Insome embodiments, as shown in FIG. 2, system 200 includes media device210, vehicle media device 220, application servers 230, map database240, and vehicle 250. In some examples, media device 210 and/or vehiclemedia device 220 may correspond to one or more of computing devices 110,140, 170 and may be in communication with one another using network 101.

In some embodiments, vehicle media device 220 may be a device withinvehicle 250, or may be part of the vehicle itself, such as an on-boardvehicle computer. The vehicle may have more than one computing device.In some examples, vehicle media device 220 may be mounted inside avehicle, such as to a dashboard of the vehicle. In some examples, thevehicle may be any type of vehicle, including a car, truck, SUV,motorcycle, scooter, SEGWAY, hoverboard, drone, bus, golf cart, train,trolley, amusement vehicle, recreational vehicle, boat, watercraft,helicopter, airplane, bicycle, and/or the like.

In some embodiments, media device 220 may include a display within ahousing. In some examples, the housing may include several parts. Insome examples, one part of the housing may include an opticallytransparent material, such as glass, and another part of the housing mayinclude other materials, such as metallic materials, e.g., aluminum,and/or plastic, which may provide a robust support structure to preventdeformation of the display.

In some embodiments, vehicle media device 220 may establishcommunication with media device 210, or vice versa. In some examples,media device 210 automatically establishes communication with vehiclemedia device 220, such as by connections between one or more ofcomputing devices 110, 140, and 170. In some examples, media device 210is automatically in communication with vehicle media device 220 viawired connection. In some examples, media device 210 may contain its ownpower supply, or may be powered by a power supply within vehicle 250. Insome examples, vehicle may charge media device 210's while in operation.In some examples, media device 210 may be charged wirelessly, e.g., on awireless charging surface, such as on a dashboard of vehicle 250.Vehicle 250 may contain a transmitter for providing energy transmissionand media device 210 may have a receiver for wireless power, wherebyenergy transfer occurs using magnetic resonant coupling. The transmittermay transmit power using multiple transmit coils and using parallelpaths from such coils to multiple receive coils in the receiver.

In some embodiments, vehicle 250 may be remotely controlled, partiallyor totally autonomous, such as partially or totally autonomous vehiclesystems and methods disclosed in U.S. Pat. No. 9,330,571, which isincorporated by reference in its entirety. In some examples, vehicle 250may contain one or more vehicle operation sensors. In some examples,media device 210 and vehicle media device 220 may be included as vehicleoperation sensors and may be configured to communicate with the one ormore external sensors. External sensors may include cameras, lasers,sonar, radar detection units (e.g., ones used for adaptive cruisecontrol), and/or the like and/or combinations thereof, and may providedata updated in real-time, updating output to reflect currentenvironment conditions. Object detection and classification forautonomous vehicles may be performed according to embodiments disclosedin U.S. Pat. No. 8,195,394, which is incorporated by reference in itsentirety.

In some embodiments, data may also be collected from other sources,including one or more application servers 230. In some examples, trafficdata may be received by one or more application servers, which mayinclude a geolocation, mapping, and navigation application such asGOOGLE MAPS, APPLE MAPS, WAZE, and/or the like and/or combinationsthereof. In some examples, application servers 230 can interact with amap or GIS database, such as map database 240, through a map applicationprogramming interface (API) such as the GOOGLE MAPS API. In someexamples, the application servers query the map or GIS database fortraffic data in response to receiving the sensor data from the mediadevice and/or host device. In some examples, map database 240 can be anSQL database. The application servers 230 can interface with one or moreservers managing the SQL database. Application data and applicationstates can be stored in a cloud managed SQL database. In some examples,map database 240 can be a document-oriented database including a NoSQLdatabase such as a MONGODB database.

FIG. 3 is an example system 300, illustrating a user interface forpresenting one or more listings on a map displayed on a wireless device,which may correspond to one or more of computing devices 110, 140, 170,media device 210, vehicle media device 220. In some examples, thewireless device may include one or more sensors, such as those sensorsdiscussed above with respect to FIG. 2. In some examples, graphical userinterface (GUI) 310 includes a map with a plurality of GUI objects thatmay be filtered based on preset information or data concerning listingspreviously requested by the first user device and/or based on listingparameters such as a proximity to the physical location of a secondlocation, such as a user location or point of interest; an availabilitytime, proximity of availability time to another reference time, a typeof listing, dimensions associated with listings, dimensions of vehiclesassociated with respective user devices, information about any hazards,whether or not the listing has a garage, information about price andtime, such as price per hour, price for various times during a day,price per day, price per week/month/year; relative size of listings, aprice range, whether or not the listing has an over-head cover, whetheror not the listing is an electric vehicle (EV) charging station; past,current, and/or future demand for the listing; a user rating, whetherparking enforcement is available, the responsiveness of parkingenforcement, and/or the like and/or a combination thereof. In someexamples, demand may be quantified based on vehicles entering andexiting a listing, based on a number of users securing listings, basedon probabilities that particular listings might be available at acertain time, based on particular destination paths of users, and/or thelike and/or a combination thereof.

In some embodiments, GUI 310 includes a map with GUI object 312, firstlisting 314, and second listings 316. In some examples, GUI object 312may be selected, and in response to the selection of GUI object 312, alist of items 320 is displayed, including one or more parameters bywhich the map of listings may be filtered. In some examples, an itemfrom list of items 320 is selected and an input is entered into a field.In some examples, selections are made via user input. In some examples,selections are made automatically without user input. In response to theinput, listings matching the selected parameters entered in one or morefields of list of items 320 may be determined. In response to thedetermination, listings within a map region are displayed on GUI 310.The map region may correspond with the current map frame, or maycorrespond with a certain radius from a current location of a wirelessdevice included in system 300.

In some embodiments, an item from list of items 320 corresponding toprice range is selected. A price range of $3-$5 is entered into a fieldcorresponding to the selected item. A plurality of listings displayed onthe map is filtered according to the selected price range. In someexamples, first listing 314 has an associated price within the selectedprice range, and second listings 316 do not have an associated pricewithin the selected price range. As a result, second listings 316 arenot displayed on the map, and only first listing 314 and other listingswith associated prices within the selected price range are displayed onthe map. Other parameters and scenarios for filtering listings that maybe displayed on the wireless device of system 300 are within the scopeof disclosed embodiments.

FIG. 4 is a flowchart illustrating an example method 400 for securing afirst listing for a first user device, determining a second listing hasa second listing value within a range different than the first listing,and securing the second listing for the first user device. Method 400 isillustrated in FIG. 4 as a set of processes 410-450. In some examples,processes 410-450 may be implemented on one or more application servers,such as application servers 230. In some examples, not all of theillustrated processes may be performed in embodiments of method 400.Additionally, one or more processes not expressly illustrated in FIG. 4may be included before, after, in between, or as part of processes410-450. In some embodiments, one or more processes 410-450 may beimplemented, at least in part, in the form of executable code stored onnon-transitory, tangible, computer readable media that when run by oneor more processors (e.g., a processor of the media device) may cause theone or more processors to perform one or more of processes 410-450. Insome examples, the first wireless device may correspond to one or moreof computing devices 110, 140, 170, media device 210, and/or vehiclemedia device 220, one or more sensors may include those sensorsdiscussed above with respect to FIG. 2, and system 300.

During a process 410, a search request from a first user device may bereceived. In some examples, the search request includes a request tosecure a first listing. In some examples, before process 410, a map oflistings may be displayed on a GUI of the first user device, such as GUI310. In some examples, the search request may also include location dataprovided by the first user device, such as global positioning system(GPS) coordinates and/or an address. In some examples, a search requestmay be sent by the first user device in response to activation ofaugmented reality (AR) or mixed reality (MR) system. In some examples, asearch request may be generated automatically. In some examples, asearch request is sent on a first user device in response to input, thatmay include touch, hand movement, voice activation, and/or the like. Insome examples, the search request includes one or more listingparameters. In some examples, the search request may include locationdata provided by the first user device, such as GPS coordinates and/oran address. The map of listings may be filtered based on presetinformation or data concerning listings previously requested by thefirst user device and/or based on listing parameters such as a proximityto the physical location of a second location, such as a user locationor point of interest; an availability time, proximity of availabilitytime to another reference time, a type of listing, dimensions associatedwith listings, dimensions of vehicles associated with respective userdevices, information about any hazards, whether or not the listing has agarage, information about price and time, such as price per hour, pricefor various times during a day, price per day, price perweek/month/year; relative size of listings, a price range, whether ornot the listing has an over-head cover, whether or not the listing is anEV charging station; past, current, and/or future demand for thelisting; a user rating, whether parking enforcement is available, theresponsiveness of parking enforcement, and/or the like and/or acombination thereof.

During a process 420, a first listing is selected from among a pluralityof listings based the search request. In some examples, one or moreapplication servers contain a database of listings. In some examples,the database of listings may be pooled from a plurality of sources, suchas city-provided data, cross-platform databases, and/or the like. Insome examples, one or more listings are secured by one or more wirelessdevices, and servers store information about each wireless device andthe corresponding secured listing. In some examples, following selectionof the first listing selection, the first user device is sent aconfirmation query. In some examples, following selection of the firstlisting selection, information about the first listing selection, suchas the time and address, are synchronized to a calendar belonging to arespective user of the first user device, such as GOOGLE CALENDAR. Arelated reservation may be automatically scheduled at such time andaddress, such as scheduling a ride using a ride sharing application. Insome examples, the first listing is selected based on listings thatmatch one or more listing parameters included in the search request,including preset information or data concerning listings previouslyrequested by the first user device, listing parameters such as aproximity to the physical location of a second location, such as a userlocation or point-of-interest; an availability time, proximity ofavailability time to another reference time, a type of listing,dimensions associated with listings, dimensions of vehicles associatedwith respective user devices, information about any hazards, whether ornot the listing has a garage, information about price and time, such asprice per hour, price for various times during a day, price per day,price per week/month/year; relative size of listings, a price range,whether or not the listing has an over-head cover, whether or not thelisting is an EV charging station; past, current, and/or future demandfor the listing; a user rating, whether parking enforcement isavailable, the responsiveness of parking enforcement, and/or the likeand/or a combination thereof. In some examples, a first listing isselected and confirmed without further response from the first userdevice. In some examples, the first listing selected may have a firstlisting value within a first value range. Calculations of assignedlisting values and ranges are elaborated upon further in discussion ofFIG. 5.

During a process 430, the database of listings is monitored as listingsparameters are updated. In some examples, the listing parameters, suchas availability times are updated as reservations are cancelled andscheduled for listings in real time. In some examples, reservations maybe held temporarily while a listing is in the process of being secured.In some examples, the database of listings is updated continuously,periodically, and/or at set location markers, including upon departureof one or more wireless devices.

During a process 440, a second listing is determined to have a secondlisting value within a second value range. In some examples, the secondvalue range is based on previously secured listings and associatedlisting parameters. In some examples, the second listing has a secondassigned value within a second value range. In some examples, the firstand second assigned values are determined based on respective listingparameters and respective weights assigned to each parameter.Calculations of assigned listing values and ranges are elaborated uponfurther in discussion of FIG. 5.

During a process 450, the second listing is secured for the first userdevice. In some examples, the first listing is no longer secured for thefirst user device, and is listed as available. In some examples, acalendar event is updated for the calendar belonging to a respectiveuser of the first user device, including information such as a time anaddress of the second listing.

FIG. 5 is a flowchart illustrating an example method 500 for determininglisting values for respective listings and securing listings withincreased listing value. Method 500 is illustrated in FIG. 5 as a set ofprocesses 510-550. In some examples, processes 510-550 may beimplemented on one or more application servers, such as applicationservers 230. In some examples, not all of the illustrated processes maybe performed in embodiments of method 500. Additionally, one or moreprocesses not expressly illustrated in FIG. 5 may be included before,after, in between, or as part of processes 510-550. In some embodiments,one or more processes 510-550 may be implemented, at least in part, inthe form of executable code stored on non-transitory, tangible, computerreadable media that when run by one or more processors (e.g., aprocessor of the media device) may cause the one or more processors toperform one or more of processes 510-550. In some examples, the firstwireless device may correspond to one or more of computing devices 110,140, 170, media device 210, and/or vehicle media device 220, one or moresensors may include those sensors discussed above with respect to FIG.2, and system 300.

During a process 510, a first listing value is determined for a firstlisting based on one or more listing parameter values associated withthe first listing and one or more weights for one or more respectivelisting parameters. In some examples, the one or more weights for one ormore respective listing parameters are determined based on userpreferences and/or previously secured listings. In some examples, theone or more listing parameter values are determined based onquantitative or qualitative characteristics associated with the listing,such characteristics including a proximity to the physical location of asecond location, such as a user location or point of interest; anavailability time, proximity of availability time to another referencetime, a type of listing, dimensions associated with listings, dimensionsof vehicles associated with respective user devices, information aboutany hazards, whether or not the listing has a garage, information aboutprice and time, such as price per hour, price for various times during aday, price per day, price per week/month/year; relative size oflistings, a price range, whether or not the listing has an over-headcover, whether or not the listing is an EV charging station; past,current, and/or future demand for the listing; a user rating, whetherparking enforcement is available, the responsiveness of parkingenforcement, and/or the like and/or a combination thereof.

A first listing value can be determined based on a summation of the oneor more weight values multiplied by the one or more respective listingparameter values. In some examples, the relationship of a listing'svalue and one or more weights associated with respective listingparameters and the one or more listing parameter values may be:

$\begin{matrix}{{\sum\limits_{n}{S_{n}w_{n}}} = y} & (1)\end{matrix}$

wherein y is the listing value, n is an index indicating a particularlisting parameter, Sn is the value for the n-th listing parameter, andWn is the weight associated with the n-th listing parameter.

In some embodiments, the relationship of a listing's value and one ormore weights associated with one or more respective listing parametersand the one or more listing parameter values may be:

ListingValue=Proximity*W1+User_Rating*W2+Time_proximity*W3+Size*W4+Price*W5 . .. SN*WN,  (2)

wherein the Listing Value is a summation of the weights 1 through Nmultiplied by the respective listing parameter values; W1 is weight one,which may multiply by the proximity parameter; W2 is weight two, whichmay multiply by the user rating parameter, and may be on a scale of 1 to5 for instance, W3 is weight three, which may multiply by the timeproximity parameter, and may be a time window of 15 to 30 minutes, inwhich earlier or later availability may be permitted; W4 is weight four,which may multiply by the size parameter; and WN is the n-th weight,which may be associated with listing parameter value, Sn. In someexamples, the proximity parameter is a distance between the listing'sphysical location and the physical location of the first user device. Insome examples, the proximity parameter is a distance between thelisting's physical location and the physical location of a destinationof the first user device or some other location. In some examples, W1 isweighted relative to a maximum distance, such as five miles, and maycorrespond to an applicable geographic area for listings. In someexamples, a parameter's value may be binary (i.e., 0 or 1) depending onwhether a particular characteristic is associated with a listing or not.In some examples, a parameter's value may be an integer (e.g., 1, 2, 3,etc.), e.g., the Size parameter value may be assigned an integerdepending on the parking space type: a compact space may be valued as 1,an SUV-sized space may be valued as 2, a large truck space may be valuedas 3, and so forth in a similar manner. In some examples, a parametersuch as Price may have a parameter value in dollars, (e.g., $4.55), orsome other currency. In some examples, the Listing Value is between1-100, and weights one through N may be weighted in order to be lessthan 100, such that the Listing Value may be a relative indicator ofvalue compared with other listings. In some examples, one more weightvalues W1 through WN may be user configurable and/or adjusted by machinelearning with inputs from past listing reservations. In some examples, auser may indicate that one or more listing parameters are more importantthan others, and the corresponding weight may be increased or decreasedaccordingly. In some examples, a user may indicate that distance isunimportant, and correspondingly, weight one may be diminished to zeroor near zero value. Similarly, a user may indicate that a parameter Snis unimportant, and then WN may be diminished to zero or near zerovalue. In some examples, a user may indicate that a parameter Sn isimportant, and then WN may be kept the same or adjusted based on theuser input. In some examples, a user may indicate that a parameter Sn isdispositive, such that a listing must have a 5 minute walking distancefrom a destination. In some examples, a user may indicate that aparameter Sn is dispositive such that no other factor matters, such thatif a listing is found that accommodates trucks, the algorithm willautomatically assign this listing with the highest value.

In some examples, one or more machine learning algorithms may beimplemented to adjust weights in order to predict more accurately whatlistings have increased relative value to a particular user or userdevice. In some examples, the determination of a listing's listing valuemay be performed using a machine learning (ML) model that may betrained/tuned based on training data collected based on positiverecognition, false recognition, and/or other criteria, such as acomparison to another listing. Although various types of machinelearning models may be deployed to refine some aspects for determining alisting's listing value, in some aspects, one or more ML basedclassification algorithms may be used. Such classifiers may include butare not limited to: a Multinomial Naive Bayes classifier, a BernoulliNaive Bayes classifier, a Perceptron classifier, a Stochastic GradientDescent (SGD) Classifier, and/or a Passive Aggressive Classifier, and/orthe like. Additionally, the ML models may be configured to performvarious types of regression, for example, using one or more variousregression algorithms, including but not limited to: a StochasticGradient Descent Regressor, and/or a Passive Aggressive Regressor, etc.

During a process 520, the first listing value is compared with one ormore second listing values corresponding to one or more second listings.In some examples, the second listing values are stored on one or moreapplication servers in a database. In some examples, the second listingvalues are previously determined based on one or more listing parametervalues and one or more weights for one or more respective listingparameters similar to process 510. As listing parameters are updated inreal time for respective listings, process 520 may be repeatedcontinuously, periodically, and/or at set location markers, includingupon listings becoming available.

During a process 530, it is determined whether the difference betweenthe first listing value and one of the second listing values exceeds apredetermined threshold. In some examples, the one or more machinelearning algorithms may be used to filter listing values with higherlisting values than the currently secured listing for a particularwireless device. In some examples, a first listing may be secured by thefirst wireless device and it is determined that there is a secondlisting with a higher listing value. At the same time, it is determinedthat the difference between the first listing value and the secondlisting value is below a predetermined threshold, such as 1-5%, alisting differential threshold. In some examples, the first listingvalue is 64, and the second listing value is 66, which is only a 3.2%increase in listing value. The second listing value may be filteredbecause it is within a first range of values, such as being less than a5% increase in listing value. In some examples, the listing differentialthreshold may be at least 1%. In some examples, if it is determined thatone of the second listings does not exceed the threshold, then themethod does not proceed to process 540. In some examples, threshold isuser configurable. In some examples, the threshold is dependent onweights. In some examples, the threshold may be time dependent. In someexamples, if the time between the securing of the first listing and thecurrent time is less than a time threshold, e.g., 5-10 minutes, then themethod does not proceed to process 540.

In some embodiments, a threshold for difference in listing value is notnecessary because factors for the threshold are taken into account inthe weighting of listing parameters. In some examples, one or moreweights for listing parameters are time dependent, and may increase,decrease, or stay the same accordingly. In some examples, when a user issecuring a listing very close to the start time of the listingreservation, the weight associated with time proximity may besignificantly increased. In some examples, there is a hierarchy of rangevalues. The first listing value may be within a first range of values.There may be a second range of values for which when it is determinedthat one of the second listing values in such range, that the methodproceeds to process 540.

Some advantages of disclosed embodiments include providing smartphoneusers with listings that are of increased listing value. Further,listings of increased value may be filtered when such listings are onlyslightly higher (e.g., <5%) than the currently secured listing or whenthe currently secured listing was very recently (e.g., <10 minutes)secured. This may prevent a smartphone user from being bothered when aparking space opens up that is only marginally better than the parkingspace they have reserved.

During a process 540, in response to determining that the differenceexceeds the predetermined threshold, a user-selectable message may beprovided on the first wireless device that secured the first listing,the user-selectable message comprising a prompt to secure the secondlisting. In some examples, the user-selectable message includes a link.In some examples, the link is a deep link, such as a uniform resourceidentifier (URI) that launches a resource within a second softwareapplication. A deep link enables the first wireless device to launch asecond application to a relevant portion within the second applicationwithout having to separately launch the second application and navigateto that relevant portion.

During a process 550, the second listing is secured by the firstwireless device. In some examples, a database of listings is updated inresponse to process 550. In some examples, process 550 may correspondwith process 450.

In some embodiments, the method 500 may be repeated starting withprocess 520. In some examples, the listing secured by a particular usermay be updated as a wireless device corresponding with the userapproaches a destination. In some examples, a particular wireless devicemay deviate from an expected path, and the time expected to reach thedestination may be updated; the listing secured by the wireless deviceis swapped for a listing that is closer in time to the time in which thewireless device is expected to reach the destination.

In some embodiments, in response to securing the second listing, one ormore machine learning algorithms adjust the one or more weights (asdiscussed above with respect to process 510) to predict more accuratelywhat listings have increased relative value to a particular user. Theone or more machine algorithms may adjust the one or more weights basedon the second listing value parameters and the listing parameter valuesof previously secured listings. The one or more machine learningalgorithms may adjust the one or more weights for groups of wirelessdevices or for individual wireless devices.

Some advantages of disclosed embodiments include providing listings toparticular smartphone users that are more suitable to that user's needs.For example, a smartphone user may have already secured a parking spacethrough a smartphone application, but may want the option to secure aneven better parking space. That smartphone user, for instance, may wanta parking spot that is cheaper and closer, and may opt-in for automaticbids for a listing that is more suitable for an associated vehicleshould one such listing become available prior to the start of thecurrently secured parking space. If such a listing is found, a user maybe presented with an option to secure a more suitable parking space forthat user's needs, and may secure the parking space with little to nointeraction with the wireless device. Listings may be filtered insituations such as when more suitable parking spaces are only slightlybetter (e.g., less than a predetermined threshold) or a more suitableparking space was already reserved recently (e.g., within a thresholdtime).

Some advantages of disclosed embodiments include providing listings toparticular smartphone users as they arrive at a destination. Forexample, a smartphone user may have a destination entered in anavigation application. As the smartphone approaches the destination,the location may be tracked. A particular listing may be booked inadvance or as the smartphone nears the destination. The listing may beswapped out for a more suitable listing as the smartphone approaches thedestination, and the destination time is updated.

FIG. 6 is an example diagram 600 illustrating conceptually how listingsmay be ranked for a first user device, represented on a GUI 610. In someexamples, GUI 610 may be displayed on a first wireless device, which maycorrespond to one or more of computing devices 110, 140, 170, mediadevice 210, vehicle media device 220, and/or system 300. In someexamples, GUI 610 includes a map with a plurality of GUI objects thatrepresent individual listings 630 and a point-of-interest 620. In someexamples, GUI 610 depicts listings 630 that are available and therelative ranking of listings 630. Listings 630 include one or moreparking spaces available for parking. In some examples, each one of thelistings 630 may include one or more listing parameters discussed abovewith respect to FIGS. 3-5. Based on the listing parameters, respectivelisting values for each of listings 630 may be determined in a mannersimilar to process 510. In some examples, the closest listing of thelistings 630 may be ranked first, and have the highest listing value oflistings 630. A little further from point-of-interest 620 is the listingranked second, and the furthest listing of listings 630 being ranked thelowest as the third. In some examples, listings 630 may be differentlyranked than depicted in FIG. 6. In some examples, the rankings aredifferent than depicted in FIG. 6 because of the relative importance ofone or more parameters other than distance, e.g., price or user rating.

In some embodiments, point-of-interest 620 may be determined based onlisting requests being above a first threshold and may be based ontraffic data. In some examples, point-of-interest 620 may be defined asa geographical location with a predefined proximity to location of anevent being or to be held. In some examples, a sports bar may beconsidered a point-of-interest around 7 pm on a Friday night, becausethe sport bar will offer a live broadcast of a baseball event at 8 pmthat same Friday night. A street intersection may be considered apoint-of-interest between 8 am to 12 pm on a Sunday morning, because afarmer market is often held at the street intersection during that timeframe. In some examples, the first threshold may be exceeded when thetraffic data indicates heavy traffic in a region. The point-of-interestcan be a location or venue causing a surge or sudden increase in demandfor parking within a vicinity of the point-of-interest. In someexamples, the point-of-interest can include a sports stadium, a concerthall, a nightclub, a movie theater, a museum, or a restaurant, farmer'smarket, a political gathering, or a parade route and/or the like and/ora combination thereof.

Some advantages of disclosed embodiments include an increase in overallparking quality. While price may be an important factor, it may not bedispositive for many users with smartphones looking for parking. Asmartphone user may be approaching a point-of-interest 620, and may belooking for parking near the destination, and would like to minimizewalking, but also park in a secure location. Based on the relativeranking of parking spaces, the space ranked first may be selected. Theparking space may be selected automatically, saving the user the hasslewith having to inspect the parking spaces. Such embodiments may increaseefficiency with respect to parking, reduce traffic, reduce accidents,diminish or eliminate the need for vehicle operator (i.e., user) input,more accurately predict parking needs, and lead to faster processing ofparking transactions. Traffic may be decreased by shortening the timespent looking for parking for drivers because less cars will be on theroad looking for parking.

FIG. 7 is a flowchart illustrating an example method 700 for determininglistings with increased listing value and facilitating a mutual exchangeof secured listings. In some examples, one or more of processes formethod 500 may be incorporated as part of method 700. In some examples,processes 710-740 may be implemented on one or more application servers,such as application servers 230. In some examples, not all of theillustrated processes may be performed in embodiments of method 700.Additionally, one or more processes not expressly illustrated in FIG. 7may be included before, after, in between, or as part of processes710-740. In some embodiments, one or more processes 710-740 may beimplemented, at least in part, in the form of executable code stored onnon-transitory, tangible, computer readable media that when run by oneor more processors (e.g., a processor of the media device) may cause theone or more processors to perform one or more of processes 710-740. Insome examples, the first wireless device may correspond to one or moreof computing devices 110, 140, 170, media device 210, and/or vehiclemedia device 220, one or more sensors may include those sensorsdiscussed above with respect to FIG. 2, and system 300.

During a process 710, a first listing value is compared with one or moresecond listing values corresponding to one or more second listings. Insome examples, process 710 corresponds with process 520.

During a process 720, a first user-selectable message is provided on afirst wireless device that secured a first listing, the first messageincluding a request to secure a second listing that is secured by asecond wireless device. In some examples, process 720 may correspondwith process 540. In some examples, a confirmation from the firstwireless device may be received. In some examples, no confirmation isreceived from the first wireless device and process 710 is repeated.

During a process 730, a second user-selectable message is provided onthe second wireless device, the second message including a request tosecure the first listing—the one secured by the first wireless device.In some examples, the first listing value of the first listing may beless than the second listing value of the second listing for the secondwireless device. In some examples, to encourage the second wirelessdevice to trade listings with the first wireless device, the secondwireless device may be provided with one or more incentive options, suchas a coupon, credit boarding pass, event ticket, voucher, store card,credit card, loyalty card, debit card, and/or the like and/orcombinations thereof. In some examples, the incentives may beproportional to a distance from the destination or time before thereservation. In some examples, the incentive may be a 20% bonus creditbecause the second wireless device traded listings at least 15 minutesbefore the reservation. In some examples, no confirmation is receivedfrom the first wireless device or second wireless device and process 710is repeated.

During a process 740, in response to receiving confirmation from boththe first wireless device and the second wireless device, the firstlisting is secured for the second wireless device and second listing issecured for the first wireless device. In some examples, weights may beadjusted as discussed above with respect to process 550. In someexamples, one listing parameter may include one or more incentiveoptions. In some examples, if a user accepts a trade for a lower valuedlisting with one or more particular incentive options, the weightassociated with the incentive option may be adjusted.

In some aspects, as a condition for a trade between two users forlisting locations, one user may be required to wait with their vehicleat a listing location until the second user arrives with their vehicle.An additional incentive may be provided to the user required to wait inorder to encourage compliance.

In some aspects, a listing value may be weighted with some accountingfor the level of parking enforcement presence. Parking enforcement maybe provided by a vendor or may be crowdsourced. In some examples, a usermay be local to a listing and act as parking enforcement by reportingthe occupancy of the vehicle in the listing. The user may take a pictureor scan the license plate of the vehicle occupying the listing andtransmit the image to the one or more application servers. If a licenseplate number is not found in the database of the one or more applicationservers, the owner or operator of the listing may be notified. Dependingon the responsiveness of enforcement to cause the vehicle to be moved,the listing's value may be increased or decreased, for instance, if thetime to move the vehicle from the time of notification is greater orless than 45 minutes.

Some advantages of disclosed embodiments include facilitating a swappingof listing reservations between users. Such technology may provide anoverall benefit to each user involved in the transaction. Other benefitsare discussed above with respect to FIGS. 4-6.

As discussed above and further emphasized here, FIGS. 1-7 are merelyexamples, and should not unduly limit the scope of the claims. Althoughillustrative embodiments have been shown and described, a wide range ofmodification, change and substitution is contemplated in the foregoingdisclosure and in some instances, some features of the embodiments maybe employed without a corresponding use of other features. One ofordinary skill in the art would recognize many variations, alternatives,and modifications. Thus, the scope of the invention should be limitedonly by the following claims, and it is appropriate that the claims beconstrued broadly and in a manner consistent with the scope of theembodiments disclosed herein.

In the foregoing description, specific details are set forth describingsome embodiments consistent with the present disclosure. It will beapparent, however, to one skilled in the art that some embodiments maybe practiced without some or all of these specific details. The specificembodiments disclosed herein are meant to be illustrative, but notlimiting. Phrases including “such as” and “for example” are intended tobe non-exclusive, and not limit embodiments to the set of things listedwithin those phrases. One skilled in the art may realize other elementsthat, although not specifically described here, are within the scope andthe spirit of this disclosure. In addition, to avoid unnecessaryrepetition, one or more features shown and described in association withone embodiment may be incorporated into other embodiments unlessspecifically described otherwise or if the one or more features wouldmake an embodiment non-functional. Furthermore, other patents and patentapplications may be incorporated by reference in order to avoidunnecessary description; the present disclosure's terms and definitionsshould take precedence over any terms and definitions used in suchreferences, when in conflict with the present disclosure.

For purposes of this disclosure, a wireless device may include anyinstrumentality or aggregate of instrumentalities operable to compute,classify, process, transmit, receive, retrieve, originate, switch,store, display, manifest, detect, record, reproduce, handle, or utilizeany form of information, intelligence, or data for business, scientific,control, entertainment, or other purposes. For example, a wirelessdevice may be a personal computer, a portable digital assistant (PDA), aconsumer electronic device, a display device or monitor, a smartphone,or any other suitable device and may vary in size, shape, performance,functionality, and price. The wireless device may include memory, one ormore processing resources such as a central processing unit (CPU) orhardware or software control logic. Additional components of thewireless device may include one or more storage devices, one or morecommunications ports for communicating with external devices as well asvarious input and output (I/O) devices, such as a keyboard, a mouse, atouchscreen, button inputs, a microphone, a motion sensor, and/or avideo display.

What is claimed is:
 1. A system, comprising: a non-transitory memory;one or more processors coupled to the non-transitory memory andconfigured to execute instructions to perform operations comprising:determining a first listing value based on one or more listingparameters associated with the first listing, the first listing beingsecured by a first wireless device; determining a second listing valuebased on one or more listing parameters associated with the secondlisting; comparing the first listing value with the second listing valueto determine a difference between the first listing value and the secondlisting value; determining whether the difference between the firstlisting value and the second listing value exceeds a predeterminedthreshold; in response to a determination that the difference exceedsthe predetermined threshold, providing a user-selectable message on thefirst wireless device, the user-selectable message comprising a promptconfigured to secure the second listing; and securing the second listingfor the first wireless device.
 2. The system of claim 1, whereinsecuring the second listing, further comprises: receiving a userselection of the second listing via the prompt; and releasing a hold onthe first listing such that the first listing is no longer secured bythe first wireless device.
 3. The system of claim 1, wherein each of theone or more listing parameters is associated with a respective weightthat is configured to be dynamically adjusted using machine learning. 4.The system of claim 1, wherein each of the one or more listingparameters associated with the second listing is associated with arespective weight that is dynamically adjusted in response to thesecuring of the second listing.
 5. The system of claim 1, wherein eachof the one or more listing parameters is associated with a respectiveweight that is dynamically adjusted based on one or more userconfigurable settings.
 6. The system of claim 1, wherein thepredetermined threshold is zero.
 7. The system of claim 1, wherein thepredetermined threshold is time dependent.
 8. The system of claim 1,wherein the user-selectable message includes a deep link.
 9. The systemof claim 1, wherein the first listing value is based on a weighted sumof the one or more listing parameters associated with the first listing.10. The system of claim 1, wherein the one or more listing parametersinclude one or more of: proximity of the first wireless device tolistings, temporal duration of reservation period, price range forlistings, or a type of listing.
 11. A computer-implemented method forsecuring listings with increased listing value, comprising: determininga first listing value based on one or more listing parameters associatedwith a first listing, the first listing being secured by a firstwireless device; determining a second listing value based on one or morelisting parameters associated with a second listing; comparing the firstlisting value with the second listing value to determine a differencebetween the first listing value and the second listing value;determining whether the difference between the first listing value andthe second listing value exceeds a predetermined threshold; in responseto a determination that the difference exceeds the predeterminedthreshold, providing a user-selectable message on the first wirelessdevice, the user-selectable message comprising a prompt configured tosecure the second listing; and securing the second listing for the firstwireless device.
 12. The computer-implemented method of claim 11,wherein securing the second listing, further comprises: receiving a userselection of the second listing via the prompt; and releasing a hold onthe first listing such that the first listing is no longer secured bythe first wireless device.
 13. The computer-implemented method of claim11, wherein each of the one or more listing parameters is associatedwith a respective weight that is configured to be dynamically adjustedusing machine learning.
 14. The computer-implemented method of claim 11,wherein each of the one or more listing parameters is associated with arespective weight that is dynamically adjusted in response to thesecuring of the second listing.
 15. The computer-implemented method ofclaim 11, wherein each of the one or more listing parameters isassociated with a respective weight that is dynamically adjusted basedon one or more user configurable settings.
 16. The computer-implementedmethod of claim 11, wherein the first listing value is based on aweighted sum of the one or more listing parameters.
 17. A system,comprising: a non-transitory memory; one or more processors coupled tothe non-transitory memory and configured to execute instructions toperform operations comprising: determining a first listing value basedon a first weighted sum of one or more listing parameters associatedwith the first listing, the first listing being secured by a firstwireless device; determining a second listing value based on a secondweighted sum of one or more listing parameters associated with thesecond listing; comparing the first listing value with the secondlisting value to determine a difference between the first listing valueand the second listing value; responsive to a determination that thefirst listing value exceeds the second listing value: providing auser-selectable message on the first wireless device, theuser-selectable message comprising a prompt configured to secure thesecond listing; and securing the second listing for the first wirelessdevice.
 18. The system of claim 17, wherein the second listing isinitially secured by a second wireless device before it is secured bythe first wireless device, and the operations further comprise providinga user-selectable message on the second wireless device, theuser-selectable message comprising a prompt configured to secure thefirst listing for the second wireless device.
 19. The system of claim18, wherein the user-selectable message includes an incentive option.20. The system of claim 17, wherein the operations further comprisedetermining that the first listing value is in a first range of listingvalues and the second listing value is in a second range of listingvalues.